from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
				from ...processing_utils import ProcessorMixin
				from ...utils import TensorType


A						=			logging.get_logger(__name__)

A						=			{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   'layoutlmv3'
		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     Optional[Any]=5_0_2_6_5     ,  snake_case__     :     str=7_6_8     ,  snake_case__     :     Optional[Any]=1_2     ,  snake_case__     :     Optional[int]=1_2     ,  snake_case__     :     Dict=3_0_7_2     ,  snake_case__     :     int="gelu"     ,  snake_case__     :     Optional[int]=0.1     ,  snake_case__     :     List[Any]=0.1     ,  snake_case__     :     Optional[Any]=5_1_2     ,  snake_case__     :     Union[str, Any]=2     ,  snake_case__     :     List[Any]=0.02     ,  snake_case__     :     List[str]=1e-5     ,  snake_case__     :     int=1     ,  snake_case__     :     Optional[int]=0     ,  snake_case__     :     str=2     ,  snake_case__     :     Union[str, Any]=1_0_2_4     ,  snake_case__     :     Union[str, Any]=1_2_8     ,  snake_case__     :     int=1_2_8     ,  snake_case__     :     List[str]=True     ,  snake_case__     :     Optional[Any]=3_2     ,  snake_case__     :     List[str]=1_2_8     ,  snake_case__     :     Any=6_4     ,  snake_case__     :     Any=2_5_6     ,  snake_case__     :     Tuple=True     ,  snake_case__     :     List[Any]=True     ,  snake_case__     :     Any=True     ,  snake_case__     :     List[str]=2_2_4     ,  snake_case__     :     Any=3     ,  snake_case__     :     Any=1_6     ,  snake_case__     :     List[Any]=None     ,  **snake_case__     :     Any     ,  )      ->	Optional[Any]:
				super().__init__(
				    vocab_size=snake_case__     ,  hidden_size=snake_case__     ,  num_hidden_layers=snake_case__     ,  num_attention_heads=snake_case__     ,  intermediate_size=snake_case__     ,  hidden_act=snake_case__     ,  hidden_dropout_prob=snake_case__     ,  attention_probs_dropout_prob=snake_case__     ,  max_position_embeddings=snake_case__     ,  type_vocab_size=snake_case__     ,  initializer_range=snake_case__     ,  layer_norm_eps=snake_case__     ,  pad_token_id=snake_case__     ,  bos_token_id=snake_case__     ,  eos_token_id=snake_case__     ,  **snake_case__     ,  )
				_lowerCamelCase				   =  max_ad_position_embeddings
				_lowerCamelCase				   =  coordinate_size
				_lowerCamelCase				   =  shape_size
				_lowerCamelCase				   =  has_relative_attention_bias
				_lowerCamelCase				   =  rel_pos_bins
				_lowerCamelCase				   =  max_rel_pos
				_lowerCamelCase				   =  has_spatial_attention_bias
				_lowerCamelCase				   =  rel_ad_pos_bins
				_lowerCamelCase				   =  max_rel_ad_pos
				_lowerCamelCase				   =  text_embed
				_lowerCamelCase				   =  visual_embed
				_lowerCamelCase				   =  input_size
				_lowerCamelCase				   =  num_channels
				_lowerCamelCase				   =  patch_size
				_lowerCamelCase				   =  classifier_dropout





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   version.parse('1.12'			)
		@property
		def   _snake_case       (       self     :     str		)      ->	Mapping[str, Mapping[int, str]]:
				# The order of inputs is different for question answering and sequence classification
				if self.task in ["question-answering", "sequence-classification"]:
						return OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'sequence'}),
						        ('bbox', {0: 'batch', 1: 'sequence'}),
						        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
						    ]		)
				else:
						return OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'sequence'}),
						        ('bbox', {0: 'batch', 1: 'sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'sequence'}),
						        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
						    ]		)
		@property
		def   _snake_case       (       self     :     List[str]		)      ->	float:
				return 1e-5
		@property
		def   _snake_case       (       self     :     Optional[int]		)      ->	int:
				return 1_2



		def   _snake_case       (       self     :     Optional[int]     ,  snake_case__     :     "ProcessorMixin"     ,  snake_case__     :     int = -1     ,  snake_case__     :     int = -1     ,  snake_case__     :     bool = False     ,  snake_case__     :     Optional["TensorType"] = None     ,  snake_case__     :     int = 3     ,  snake_case__     :     int = 4_0     ,  snake_case__     :     int = 4_0     ,  )      ->	Mapping[str, Any]:
				setattr(processor.image_processor     ,  'apply_ocr'     ,  snake_case__		)

				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				_lowerCamelCase				   =  compute_effective_axis_dimension(
				    snake_case__     ,  fixed_dimension=OnnxConfig.default_fixed_batch     ,  num_token_to_add=0		)
				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				_lowerCamelCase				   =  processor.tokenizer.num_special_tokens_to_add(snake_case__		)
				_lowerCamelCase				   =  compute_effective_axis_dimension(
				    snake_case__     ,  fixed_dimension=OnnxConfig.default_fixed_sequence     ,  num_token_to_add=snake_case__		)
				# Generate dummy inputs according to compute batch and sequence
				_lowerCamelCase				   =  [[' '.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

				# Generate dummy bounding boxes
				_lowerCamelCase				   =  [[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size

				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
				_lowerCamelCase				   =  self._generate_dummy_images(snake_case__     ,  snake_case__     ,  snake_case__     ,  snake_case__		)

				_lowerCamelCase				   =  dict(
				    processor(
				        snake_case__     ,  text=snake_case__     ,  boxes=snake_case__     ,  return_tensors=snake_case__     ,  )		)

				return inputs
def 	lowerCamelCase					(	UpperCamelCase     :				int							)   ->    bool:
		_lowerCamelCase				   =  (1 + 24 * n) ** 0.5
		return ((1 + root) / 6) % 1 == 0


def 	lowerCamelCase					(	UpperCamelCase     :				int = 50_00							)   ->    int:
		_lowerCamelCase				   =  [(i * (3 * i - 1)) // 2 for i in range(1			,       UpperCamelCase							)]
		for i, pentagonal_i in enumerate(UpperCamelCase							):
				for j in range(UpperCamelCase			,       len(UpperCamelCase							)							):
						_lowerCamelCase				   =  pentagonal_nums[j]
						_lowerCamelCase				   =  pentagonal_i + pentagonal_j
						_lowerCamelCase				   =  pentagonal_j - pentagonal_i
						if is_pentagonal(UpperCamelCase							) and is_pentagonal(UpperCamelCase							):
								return b

		return -1


if __name__ == "__main__":
				print(F'''{solution() = }''')